Global Exponential Stability of Impulsive Cohen-Grossberg-Type BAM Neural Networks with Time-Varying and Distributed Delays

Global Exponential Stability of Impulsive Cohen-Grossberg-Type BAM Neural Networks with Time-Varying and Distributed Delays

The purpose of this paper is to investigate the global exponential stability of a class of impulsive bidirectional associative memories (BAM) neural networks that possesses Cohen-Grossberg dynamics. By constructing and using some inequality techniques and a fixed point theorem sufficient conditions...

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Veröffentlicht in:International journal of applied physics and mathematics 2014-05, Vol.4 (3), p.196-200
Hauptverfasser: Akca, Haydar, Benbourenane, Jamal, Covachev, Valery
Format: Artikel
Sprache:eng
Schlagworte:
Associative memory
Delay
Dynamic tests
Inequalities
Mathematical analysis
Neural networks
Stability
Time delay
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Zusammenfassung:The purpose of this paper is to investigate the global exponential stability of a class of impulsive bidirectional associative memories (BAM) neural networks that possesses Cohen-Grossberg dynamics. By constructing and using some inequality techniques and a fixed point theorem sufficient conditions are obtained to ensure the existence and global exponential stability of the solutions for impulsive Cohen-Grossberg neural networks with time delays and distributed delays.
ISSN:2010-362X
2010-362X
DOI:10.7763/IJAPM.2014.V4.282